Advanced Data Science Professional (ADSP)

Course Outline

Data Science is a discipline on data, its application, and how it can be utilized to help companies and professionals to make better decisions. In this module, participants will be introduced to the core components of data science. Participants will also learn about how to craft a data science strategy that is both efficient and reliable.

Topics Covered

What is Data Science Data Science Vs. Analytics What is Data warehouse Online Analytical Processing (OLAP) MIS Reporting

Data Science and its Industry Relevance Problems and Objectives in Different Industries How to Harness the power of Data Science? ELT vs ETL

Python Programming language is one of the most accessible and flexible programming languages available. The syntax is simplified and can be easily written. With its extensive libraries, it is no wonder that data professionals are using Python as their preferred language. In this module, participants will learn the fundamentals of Python Programming and some of its popular libraries for Data Science

Topics Covered

Python Editors & IDE Custom Environment Settings Basic Rules in Python Most Common Packages / Libraries in Python (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) Tuples, Lists, Dictionaries)

List and Dictionary Comprehensions Variable & Value Labels –  Date & Time Values Basic Operations - Mathematical - string - date Reading and writing data Simple plotting/Control flow/Debugging/Code profiling

Reading / writing data one of the first steps to kickstarting any Data Science project. Participants will have the chance to experience first-hand how to read / write data from various sources

Topics Covered

Importing Data into from Various sources Database Input (Connecting to database)

Viewing Data objects - sub setting, methods Exporting Data to various formats

The accuracy and quality of any analysis depend on how well the data is being cleaned. Data cleaning can be used to detect and correct errors or anomalies in the data. In this module, participants will learn the art of data cleaning to enhance the quality of data.

Topics Covered

Cleaning of Data with Python Steps to Data Manipulation Python Tools for Data manipulation User Defined Functions in Python

Stripping out extraneous information Normalization of Data and Data Formatting Important Python Packages e.g.Pandas, Numpy etc)

Data visualization helps people see, interact, and understand data better. The right visualization can help align the understanding of various stakeholders. Participants will have the opportunity to learn how to use Python Programming to perform Data Visualization.

Topics Covered

Exploratory Data Analysis Descriptive Statistics, Frequency Tables and Summarization Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis) Creating Graphs Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Statistics are being used by Data Scientists to gather, review, analyze, and draw conclusions from data. Statistics are the core of machine learning algorithms, capturing, and translating data patterns into actionable evidence.

Topics Covered

Basic Statistics - Measures of Central Tendencies and Variance Building blocks (Probability Distributions, Normal distribution, Central Limit Theorem) Inferential Statistics (Sampling, Concept of Hypothesis Testing)

Statistical Methods: Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square Statistical Methods: ANOVA Statistical Methods: Correlation and Chi-square

Machine learning is a field of study that gives computers the ability to learn without being programmed extensively. It applies algorithms to process the data and get trained for delivering future predictions. In this module, participants will be introduced to the key concepts of Machine Learning and how it can be easily applied using Python Programming.

Topics Covered

Statistical Learning vs Machine Learning Iteration and Evaluation Supervised Learning vs Unsupervised Learning

Predictive Modelling - Data Pre-processing, Sampling, Model Building, Validation Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics Cross ValidationTrain & Test, Bootstrapping, K-Fold validation etc

A statistical technique derived from data mining, machine learning, and predictive modeling by using data from past events to predict the future. Participants will experience first-hand how predictive analytics can be deployed using Python Programming.

Topics Covered

Introduction to Predictive Modelling Types of Business Problems Mapping of Techniques Linear Regression

Logistic Regression Segmentation - Cluster Analysis (K-Means / DBSCAN) Decision Trees (CHAID/CART/CD 5.0) Time Series Forecasting

A/B testing is a framework that lets you set up your experiment to compare the performance of two versions of the same thing. It helps teams validate key questions, establishes causality, and helps data-driven teams looking to optimize their products.

Topics Covered

Topics Covered

Introduction to A/B Testing Measuring Conversion for A/B Testing/li> T-Test and P-Value

Measuring T-Statistics and P-Values using Python A/B Test Gotchas Novelty Effects, Seasonal Effects, and Selection of Bias Data Pollution

Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions